Advances in Computational Intelligence Systems

Advances in Computational Intelligence Systems
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Contributions Presented at the 22nd UK Workshop on Computational Intelligence (UKCI 2023), September 6–8, 2023, Birmingham, UK
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Artikel-Nr:
9783031475085
Veröffentl:
2024
Einband:
eBook
Seiten:
670
Autor:
Nitin Naik
Serie:
1453, Advances in Intelligent Systems and Computing
eBook Typ:
PDF
eBook Format:
Reflowable eBook
Kopierschutz:
Digital Watermark [Social-DRM]
Sprache:
Englisch
Beschreibung:

This book comprises the papers presented at the 22nd UK Workshop on Computational Intelligence (UKCI 2023), held at Aston University, Birmingham, the UK, during 6 to 8 September 2023. UKCI is the flagship AI conference of the UK and Europe held annually. In UKCI 2023, a total of 63 papers were received from 11 countries including the UK, India, Germany, China, Japan, Australia, Italy, Thailand, Turkey, Jordan and Saudi Arabia; of these, 50 were accepted for presentation. The review process was rigorous, wherein three reviews were provided for all papers, with some having four reviews. This book highlights the latest research developments in computational intelligence and its applications. It covers both conventional and advanced techniques in fuzzy systems, neural networks, evolutionary computation, machine learning, deep learning, federated learning, data mining, cognitive computing, intelligent robotics, optimization methods, hybrid methods, and applications of computational intelligence. The book offers a valuable reference guide for readers with expertise in computational intelligence or who are seeking a comprehensive and timely review of the latest trends in computational intelligence. Special emphasis is placed on novel methods and their use in a wide range of application areas, updating both academics and professionals on the state of the art.

This book comprises the papers presented at the 22nd UK Workshop on Computational Intelligence (UKCI 2023), held at Aston University, Birmingham, the UK, during 6 to 8 September 2023. UKCI is the flagship AI conference of the UK and Europe held annually. In UKCI 2023, a total of 63 papers were received from 11 countries including the UK, India, Germany, China, Japan, Australia, Italy, Thailand, Turkey, Jordan and Saudi Arabia; of these, 50 were accepted for presentation. The review process was rigorous, wherein three reviews were provided for all papers, with some having four reviews. This book highlights the latest research developments in computational intelligence and its applications. It covers both conventional and advanced techniques in fuzzy systems, neural networks, evolutionary computation, machine learning, deep learning, federated learning, data mining, cognitive computing, intelligent robotics, optimization methods, hybrid methods, and applications of computational intelligence. The book offers a valuable reference guide for readers with expertise in computational intelligence or who are seeking a comprehensive and timely review of the latest trends in computational intelligence. Special emphasis is placed on novel methods and their use in a wide range of application areas, updating both academics and professionals on the state of the art.

Towards Reinforcement Learning for Non-Stationary Environments.- Detection of Cyberbullying on Social Media Platforms using Machine Learning.- The Changing Landscape of Machine Learning: A Comparative Analysis of Centralized Machine Learning, Distributed Machine Learning and Federated Machine Learning.- Privacy and Security Landscape of Metaverse.- Strategies to Apply Genetic Programming Directly to the Traveling Salesman Problem.- A Comparative Analysis of GPT-3 and BERT Models for Text-based Emotion Recognition: Performance, Efficiency, and Robustness.- An Intrusion Detection System using the XGBoost Algorithm for SDVN.- Hybridizing L´evy Flights and Cartesian Genetic Programming for Learning Swarm-based Optimization.- An Evaluation of Handwriting Digit Recognition using Multilayer SAM Spiking Neural Network.- Towards Accurate Rainfall Volume Prediction: An Initial Approach with Deep Learning, Advanced Feature Selection, Parameter Optimisation, and Ensemble Techniques for Time-SeriesForecasting.

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